In the word embedding association test (WEAT, [1]}), the association of two complementary classes of words, e.g., European names and African names, with two other complementary classes of attributes that indicate bias, e.g., pleasant and unpleasant attributes, are studied to quantify the bias.
The bias is defined as the difference in the degree with which European names are associated with pleasant and unpleasant attributes in comparison with African names being associated with pleasant and unpleasant attributes.
Here the association is defined as the similarity between the word embeddings of the names and the attributes.
This is the first large scale study that showed word embeddings exhibit several stereotypical biases and not just gender bias.
Our inspiration for CAT comes from WEAT.
